Transfer learning on protein language models improves antimicrobial peptide classification.

ABSTRACT: Antimicrobial peptides (AMPs) are essential components of the innate immune system in humans and other organisms, exhibiting potent activity against a broad spectrum of pathogens. Their potential therapeutic applications, particularly in combating antibiotic resistance, have rendered AMP classification a vital task in computational biology. However, the scarcity of labeled AMP sequences, coupled with the diversity and complexity of AMPs, poses significant challenges for the training of standalone AMP classifiers. Self-supervised learning has emerged as a powerful paradigm in addressing such challenges across various fields, leading to the development of Protein Language Models (PLMs). These models leverage vast amounts of unlabeled protein sequences to learn biologically relevant features,

SUBMITTER: Georgoulis E 

PROVIDER: S-EPMC12559171 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature